Merge pull request #302 from ranamanish674zu/newfeature

Job Satisfaction Analysis

Author: sanjay-kv

Job Satisfaction Analysis/DatasetLink.txt

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+https://drive.google.com/file/d/1CnCnFGRt0LDJibsDuH2690gTPpcB-RJx/view?usp=sharing

Job Satisfaction Analysis/JobSatisfaction.ipynb

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+import streamlit as st
+import joblib
+import pandas as pd
+
+# Load the model
+model = joblib.load('model.pkl')
+
+# Define the feature columns used in the model
+features = ['Hobby', 'OpenSource', 'Country', 'Student', 'Employment', 'FormalEducation',
+            'UndergradMajor', 'CompanySize', 'DevType', 'YearsCoding', 'YearsCodingProf']
+
+st.title('Job Satisfaction Prediction')
+
+# Create a form for user input
+with st.form(key='prediction_form'):
+    Hobby = st.selectbox('Hobby:', ['Yes', 'No'])
+    OpenSource = st.selectbox('Open Source:', ['Yes', 'No'])
+    Country = st.selectbox('Country:', ['United States', 'India', 'Germany'])
+    Student = st.selectbox('Student:', ['Yes', 'No'])
+    Employment = st.selectbox('Employment:', ['Employed full-time', 'Employed part-time', 'Self-employed', 'Unemployed'])
+    FormalEducation = st.selectbox('Formal Education:', ["Bachelor’s degree (BA, BS, B.Eng., etc.)", 
+                                                         "Master’s degree (MA, MS, M.Eng., MBA, etc.)", 
+                                                         "Doctoral degree (PhD)"])
+    UndergradMajor = st.selectbox('Undergrad Major:', ["Computer science, computer engineering, or software engineering", 
+                                                       "Information technology, networking, or system administration", 
+                                                       "Other engineering discipline"])
+    CompanySize = st.selectbox('Company Size:', ['Fewer than 10 employees', '10 to 19 employees', '20 to 99 employees', 
+                                                 '100 to 499 employees', '500 to 999 employees', '1,000 to 4,999 employees'])
+    DevType = st.selectbox('Dev Type:', ['Developer, back-end', 'Developer, front-end', 'Developer, full-stack'])
+    YearsCoding = st.selectbox('Years Coding:', ['0-2 years', '3-5 years', '6-8 years', '9-11 years'])
+    YearsCodingProf = st.selectbox('Years Coding Professionally:', ['0-2 years', '3-5 years', '6-8 years', '9-11 years'])
+    
+    submit_button = st.form_submit_button(label='Get Prediction')
+
+if submit_button:
+    # Collect user input
+    input_data = {
+        'Hobby': Hobby,
+        'OpenSource': OpenSource,
+        'Country': Country,
+        'Student': Student,
+        'Employment': Employment,
+        'FormalEducation': FormalEducation,
+        'UndergradMajor': UndergradMajor,
+        'CompanySize': CompanySize,
+        'DevType': DevType,
+        'YearsCoding': YearsCoding,
+        'YearsCodingProf': YearsCodingProf
+    }
+    
+    # Convert user input to DataFrame
+    input_df = pd.DataFrame([input_data])
+
+    # Ensure the input has the same columns as the training data
+    input_df = input_df[features]
+
+    # Make prediction
+    prediction = model.predict(input_df)
+
+    # Display the prediction
+    st.write(f'Predicted Job Satisfaction: {prediction[0]}')

Job Satisfaction Analysis/app.py

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+here are requirement->
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler, OneHotEncoder
+from sklearn.compose import ColumnTransformer
+from sklearn.pipeline import Pipeline
+from sklearn.impute import SimpleImputer
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import accuracy_score, confusion_matrix, classification_report